Computational Analysis
The proposed method requires only one forward pass of the original test image itself along with its augmented versions, and thus the inference time is very close to the source model itself. We report the average computation time for all methods on the SYNTHIA [1]} \(\rightarrow \)  Cityscapes setting in Figure REF .
This computation time also includes the time required to compute the necessary augmentations.
Compared to TENT [2]} which is currently the state-of-the-art TTA method, our method is  24% faster with a 9.6% relative improvement (Table REF ) in performance. The speed of our method can be further improved by embedded implementations for better usage of GPUs. The simplicity of inference in the proposed method and its performance shows how universally applicable it is for any deployed application, as it conforms to the SITA setting.
